70 citations · 70 across the 15 of their papers we have counts for
15 papers
CardioMOD-Net: A Modal Decomposition-Neural Network Framework for Diagnosis and Prognosis of HFpEF from Echocardiography Cine Loops
Andrés Bell-Navas, Jesús Garicano-Mena, Antonella Ausiello +3
Introduction: Heart failure with preserved ejection fraction (HFpEF) arises from diverse comorbidities and progresses through prolonged subclinical stages, making early diagnosis a…
A Critical Assessment of Pattern Comparisons Between POD and Autoencoders in Intraventricular Flows
Eneko Lazpita, Andrés Bell-Navas, Jesús Garicano-Mena +2
Understanding intraventricular hemodynamics requires compact and physically interpretable representations of the underlying flow structures, as characteristic flow patterns are clo…
MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
Guillermo Barragán, Ashton Hetherington, Arindam Sengupta +3
We introduce MoTIF, a mode-structured tensor framework for multi-parametric approximation, super-resolution, and temporal forecasting of high-dimensional unsteady systems. The meth…
Efficient Reduced Order Modeling Based on HODMD to Predict Intraventricular Flow Dynamics
Eneko Lazpita, Jesus Garicano-Mena, Soledad Le Clainche
Accurate and efficient modeling of cardiac blood flow is crucial for advancing data-driven tools in cardiovascular research and clinical applications. Recently, the accuracy and av…
Characterizing Intraventricular Flow Patterns via Modal Decomposition Techniques in Idealized Left Ventricle Models
Eneko Lazpita, Michael Neidlin, Jesus Garicano-Mena +1
Understanding the formation, propagation, and breakdown of the main vortex ring (VR) is essential for characterizing left ventricular (LV) hemodynamics, as its dynamics have been l…
HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics
Guillermo Barragán, Ashton Hetherington, Rodrigo Abadía-Heredia +2
In this work we present a novel methodology that combines Higher Order Singular Value Decomposition (HOSVD) with Deep Learning (DL) techniques for super-resolution in computational…
Heart Failure Prediction using Modal Decomposition and Masked Autoencoders for Scarce Echocardiography Databases
Andrés Bell-Navas, María Villalba-Orero, Enrique Lara-Pezzi +2
Heart diseases remain the leading cause of mortality worldwide, implying approximately 18 million deaths according to the WHO. In particular, heart failures (HF) press the healthca…
Modeling heart flow dynamics using numerical simulations to identify the vortex ring: a practical guide
Eneko Lazpita, Andrea Mares, Pedro Quintero +2
In this study, we present a comprehensive numerical analysis of blood flow within human left ventricle models, with particular emphasis on optimizing simulation conditions to enhan…
EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach
Nourelhouda Groun, Maria Villalba-Orero, Lucia Casado-Martin +4
In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the field faces sign…
A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved Cardiac Disease Identification
Nourelhouda Groun, Maria Villalba-Orero, Lucia Casado-Martin +4
In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to impr…
Review on vortex dynamics in the left ventricle as an early diagnosis marker for heart diseases and its treatment outcomes
Mahesh S. Nagargoje, Eneko Lazpita, Jesús Garicano-Mena +1
The heart is the central part of the cardiovascular network. Its role is to pump blood to various body organs. Many cardiovascular diseases occur due to an abnormal functioning of…
Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Andrés Bell-Navas, Nourelhouda Groun, María Villalba-Orero +3
Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering th…
Data-driven modal decomposition methods as feature detection techniques for flow problems: a critical assessment
Beka Begiashvili, Nourelhouda Groun, Jesús Garicano-Mena +2
Modal decomposition techniques are showing a fast growth in popularity for their good properties as data-driven tools. There are several modal decomposition techniques, yet Proper…
A Novel Data-Driven Method for the Analysis and Reconstruction of Cardiac Cine MRI
Nourelhouda Groun, Maria Villalba-Orero, Enrique Lara-Pezzi +3
Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed informatio…
Higher Order Dynamic Mode Decomposition: from Fluid Dynamics to Heart Disease Analysis
Nourelhouda Groun, Maria Villalba-Orero, Enrique Lara-Pezzi +3
In this work, we study in detail the performance of Higher Order Dynamic Mode Decomposition (HODMD) technique when applied to echocardiography images. HODMD is a data-driven method…